2019
DOI: 10.1103/physrevlett.122.015503
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Temperature Rise Associated with Adiabatic Shear Band: Causality Clarified

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Cited by 135 publications
(34 citation statements)
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References 45 publications
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“…Combined with standard lattice and shear wave measurements for copper, the synchronisation time for the largest avalanche is predicted to lie in the range 2 to 80 μs, depending on the exact dimensions of its axes. The predicted synchronisation times are consistent with the observed time between avalanches in acoustic emission 10 and the delay between stress collapse and temperature rise in adiabatic shear banding 52 . Whilst the geometric prefactors presented are specific to the Eshelby inclusion, the L 3 C s −1 scaling is general to all 3D precursors, as is the spatial limitation to L, consequently all 3D mechanisms will have similar synchronisation times and in SOC, the same scaling law.…”
Section: Resultssupporting
confidence: 81%
“…Combined with standard lattice and shear wave measurements for copper, the synchronisation time for the largest avalanche is predicted to lie in the range 2 to 80 μs, depending on the exact dimensions of its axes. The predicted synchronisation times are consistent with the observed time between avalanches in acoustic emission 10 and the delay between stress collapse and temperature rise in adiabatic shear banding 52 . Whilst the geometric prefactors presented are specific to the Eshelby inclusion, the L 3 C s −1 scaling is general to all 3D precursors, as is the spatial limitation to L, consequently all 3D mechanisms will have similar synchronisation times and in SOC, the same scaling law.…”
Section: Resultssupporting
confidence: 81%
“…Many researchers have attempted to develop methods to infer causality from observational data over for several years (Pearl, 1988b(Pearl, , 2000Neapolitan et al, 2004). While there have been some notable contributions in the field demonstrating the plausibility of learning causality from non-experimental data (Granger, 1969;Sims, 1972;Pearl, 2000), learning structural causal models from observational data is still a challenge (Guo et al, 2019). Recent advances in the field of discovering causality has looked at learning Causal Bayesian Network (CBN).…”
Section: Introductionmentioning
confidence: 99%
“…Latest research 18 revealed that temperature rise cannot be the cause of adiabatic shear band. Rather, it might be the result of adiabatic shear localization.…”
Section: Introductionmentioning
confidence: 99%